论文标题
在信息鲁棒性下依次最佳定价
Sequentially Optimal Pricing under Informational Robustness
论文作者
论文摘要
卖方会随着时间的推移出售一个物体，但不确定买家如何学习付费的意愿。我们考虑\ textit {有限承诺}下的信息鲁棒性，其中卖方提供了一个价格\ textit {每个时期}，以最大程度地利用持续利润，而不是最差的信息到达。我们的公式通过考虑最坏的情况\ textit {rectientally}来保持动态一致性。在一般条件下，我们表征了基本独特的平衡，在该平衡中，买家不会延迟以后学习更多。此外，我们确定了确保均衡价格路径的条件是``加强''，因此即使动态不一致的信息到达也不会将卖方的收益降低到均衡水平以下。
A seller sells an object over time but is uncertain how the buyer learns their willingness-to-pay. We consider informational robustness under \textit{limited commitment}, where the seller offers a price \textit{each period} to maximize continuation profit against worst-case information arrival. Our formulation maintains dynamic consistency by considering the worst case \textit{sequentially}. Under general conditions, we characterize an essentially unique equilibrium where the buyer does not delay to learn more later. Furthermore, we identify a condition that ensures the equilibrium price path is ``reinforcing,'' so even dynamically inconsistent information arrival would not lower the seller's payoff below the equilibrium level.
